Gender: Female 
Age: 25-34
Nationality: United States
Ethnicity: White / Caucasian 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Undergraduate degree
Mother's education: Undergraduate degree
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 6
Home: Own 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Homemaker
Father's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Hobbies: Listen to rock/indie music;Exercise;Use social media 
Devices used daily: Laptop;Smartphone;Tablet;Smartwatch 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools they would find useful: Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text) 
Frequency of LLM use: Sometimes
LLMs used: ChatGPT;GitHub Copilot;Claude 
LLM use cases: Coding;Translation;Proofreading/Editing (e.g., grammar, spelling, structure improvements);Generate art 
Contexts of LLM use: Work;Entertainment;Creative or artistic 
Prompt 1: Help me create C++ program that implements a database of student records. It consists of two parts: an unsorted vector of student records, and an index of student IDs implemented as a closed hash table. 
Prompt 2: In an Elgammal Cryptosystem, Let f(x) be a monic irreducible polynomial of degree 3 in Z3[x] and hence F = Z3[x]/(f(x)) is a finite field with 26 non zero-elements. We then associate the 26 letters of the alphabet with the 26 non-zero elements in F where A = 1, B =2, C= 3 ... X=24, Y=25, Z=26. To obtain the corresponding polynomials in F, replace each number with x. For example, N = 14 = (1 x (3^2)) + (1 x 3) + 2 = (x^2) + x + 2 in F and 2(x^2) + 1 = (2 x (3^2)) + 1 = 19 = J.  Let f(x) = (x^3) + 2x + 2, alpha = 2x + 2, and a = 9. Find beta = (alpha^9)
Prompt 3: The probabiliity that John watches a certain show is .4. The probability that Jane watches a certain show is .5. The probability that John watches the show given that Jane does is .7. Find the probability that both John and Jane watch the show.
Prompt 4: Suppose you have a closed hash table with 500 slots, and there are currently 400 records in the hash table.  Approximately how many memory accesses, on average, would you expect upon inserting the next record into the table, assuming a "good" (near optimal) hash function and probe function?
Prompt 5: Use a = 11 to decrypt ciphertext (K, H)